Density-Based Clustering

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15 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphs
Aug 7, 2026Yuning Yu, José Rodríguez-Piñeiro, Xuefeng Yin +1Clustering Hierarchical Clustering
Tongji University, Shanghai, China · RadioSky (Shanghai) Communication Technology Co., Ltd, Shanghai, China
The K-SCAN Clustering Algorithm
Jul 27, 2026Filip Kosiorowski, Grzegorz SrokaClustering Unsupervised Clustering
The Faculty of Mathematics and Applied Physics, Rzeszów University of Technology, Powstańców Warszawy 12, 35-959 Rzeszów, Poland · Department of Analysis Nonlinear, Rzeszów University of Technology, Powstańców Warszawy 12, 35-959 Rzeszów, Poland
ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material
May 28, 2026Pernille Matthews, Lena Krieger, Tommaso Amico +3Explainable Artificial Intelligence Counterfactual Explanations
LMU Munich, DBS, MCML Munich, Germany
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
May 21, 2026Tianyun Zhang, Zhen Yang, Haozhao Wang +2Federated Learning Aggregation Byzantine-Robust Federated Learning
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China · School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China · Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Towards Robust and Scalable Density-based Clustering via Graph Propagation
May 1, 2026Yingtao Zheng, Hugo Phibbs, Ninh PhamGraph-Based Label Propagation Clustering
School of Computer Science, University of Auckland, New Zealand. · Department of Mathematics and Computer Science, University of Southern Denmark, Denmark.
How Hard Is Continuous Clustering? Lower Bounds from the Existential Theory of the Reals
Apr 24, 2026Angshul MajumdarClustering Density-Based Clustering
IIIT Delhi
ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation
Apr 14, 2026Gibson Nkhata, Uttamasha Anjally Oyshi, Quan Mai +1Density-Based Clustering Retrieval-Augmented Generation
University of Arkansas, Fayetteville, AR 72701, USA · Walmart Inc., Bentonville, AR 72716, USA
Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees
Apr 8, 2026Marek GagolewskiDensity-Based Clustering Clustering
Warsaw University of Technology, Faculty of Mathematics and Information Science, ul. Koszykowa 75, 00-662 Warsaw, Poland · Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
Deep Image Clustering Based on Curriculum Learning and Density Information
Mar 31, 2026Haiyang Zheng, Ruilin Zhang, Hongpeng WangDensity-Based Clustering Clustering
Harbin Institute of Technology, Shenzhen, Shenzhen, China · Harbin Institute of Technology, Shenzhen & Peng Cheng Laboratory, Shenzhen, China
Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors
Mar 24, 2026Max Marriott-Clarke, Lazar Novakovic, Elizabeth Ratzer +3Density-Based Clustering 3D Point Cloud Segmentation
Blackett Laboratory, Imperial College London, UK · Department of Physics & Astronomy, Brown University, USA · Brown Center for Theoretical Physics and Innovation (BCTPI), Brown University, USA
Discovery of Bimodal Drift Rate Structure in FRB 20240114A: Evidence for Dual Emission Regions
Mar 18, 2026Santosh ArronDensity-Based Clustering
Blankline, blankline.org
AdaBox: Adaptive Density-Based Box Clustering with Parameter Generalization
Mar 6, 2026Ahmed ElmahdiDensity-Based Clustering Clustering
Independent Researcher
Scalable Posterior Uncertainty for Flexible Density-Based Clustering
Mar 3, 2026Nicola Bariletto, Stephen G. WalkerDensity-Based Clustering Clustering
Department of Statistics and Data Sciences, UT Austin
Doubly Stochastic Mean-Shift Clustering
Feb 17, 2026Tom Trigano, Yann Sepulcre, Itshak LapidotClustering Unsupervised Clustering
Shamoon College of Engineering, Department of Electrical Engineering, Ashdod, Israel · Sapir Academic College, Department of Engineering, Sderot, Israel · Afeka Tel-Aviv Academic College of Engineering, School of Electrical Engineering, Israel +1
Persistent Multiscale Density-based Clustering
Dec 18, 2025Daniël Bot, Leland McInnes, Jan AertsClustering Unsupervised Clustering
1UHasselt, Data Science Institute (DSI) · 2Tutte Institute for Mathematics and Computing · 3KU Leuven, Augmented Intelligence for Data Analytics Lab, Department of Biosystems

Aug 7, 2026J/K move · Enter open · S save

Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphs

Yuning Yu, José Rodríguez-Piñeiro, Xuefeng Yin, Bin Feng

Tongji University, Shanghai, China · RadioSky (Shanghai) Communication Technology Co., Ltd, Shanghai, China

Clustering Hierarchical Clustering Graph Clustering Density-Based Clustering

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Clustering is a fundamental data mining technique for pattern recognition through unsupervised learning. Among various clustering methods, hierarchical clustering, density-based clustering, and graph clustering stand out as representative approaches. For hierarchical clustering, it can be categorized into agglomerative and divisive modes to construct clusters in a recursive manner. The key aspect of both modes is the calculation of inter-cluster similarity, which determines whether to merge the sub-clusters into one cluster or divide a current cluster into sub-clusters. Traditionally, the similarity is derived from pairwise distances, often overlooking density variations and structural connectivity in graphs. To address this, we propose a density-aware hierarchical clustering method based on element-categorized connection subgraphs (DHC-ECS), which effectively integrates the hierarchical clustering, density-based clustering, and graph clustering. Particularly, a novel inter-cluster similarity metric is introduced that considers not only distances but also the element categorization in the KNN connection subgraphs, kernel density estimation, and local connectivity within sub-clusters. Extensive evaluations on heterogeneous benchmark datasets demonstrate that DHC-ECS exhibits superior overall performance in terms of clustering accuracy and parameter robustness compared with the baseline methods (including AChameleon, RNN-DBSCAN, McDPC, and G-RMS). The work indicates the great potential of the proposed clustering algorithm for low-dimensional datasets by leveraging local density and graph-structured connectivity (i.e., the duality of vertices and edges), as well as the possibility to determine an intrinsic threshold, reducing the reliance on manual parameter tuning.